{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None) # show all columns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import helperFunctions\n",
    "\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the train and test csv files\n",
    "train_df = pd.read_csv('imputed_data/train.csv')\n",
    "test_df = pd.read_csv('imputed_data/test-3.csv')\n",
    "selected_columns = [ 'AwayTeam', 'Date','HomeTeam', 'league']\n",
    "train_df = train_df[selected_columns]\n",
    "test_df = test_df[selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7131/7131 [00:43<00:00, 165.69it/s]\n",
      "100%|██████████| 7131/7131 [01:44<00:00, 68.37it/s]\n",
      "100%|██████████| 7131/7131 [01:30<00:00, 78.74it/s]\n",
      "100%|██████████| 7131/7131 [00:00<00:00, 116220.97it/s]\n",
      "100%|██████████| 7131/7131 [01:31<00:00, 77.63it/s]\n",
      "100%|██████████| 7131/7131 [00:00<00:00, 219167.59it/s]\n",
      "100%|██████████| 1784/1784 [00:10<00:00, 169.19it/s]\n",
      "100%|██████████| 1784/1784 [00:25<00:00, 69.87it/s]\n",
      "100%|██████████| 1784/1784 [00:22<00:00, 79.85it/s]\n",
      "100%|██████████| 1784/1784 [00:00<00:00, 230079.28it/s]\n",
      "100%|██████████| 1784/1784 [00:23<00:00, 76.26it/s]\n",
      "100%|██████████| 1784/1784 [00:00<00:00, 194082.02it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "def extract_and_summarize(df):\n",
    "    df[\"Date\"] = pd.to_datetime(df[\"Date\"], format=\"%Y-%m-%d\").dt.strftime(\n",
    "        \"%d/%m/%Y\"\n",
    "    )\n",
    "    df[\"B365H\"], df[\"B365D\"], df[\"B365A\"] = zip(\n",
    "        *df.progress_apply(helperFunctions.extractMatchOdds, axis=1)\n",
    "    )\n",
    "    (\n",
    "        df[\"HPr5MW\"],\n",
    "        df[\"HPr5MD\"],\n",
    "        df[\"HPr5ML\"],\n",
    "        df[\"APr5MW\"],\n",
    "        df[\"APr5MD\"],\n",
    "        df[\"APr5ML\"],\n",
    "    ) = zip(*df.progress_apply(helperFunctions.numWDLInLastFiveMatches, axis=1))\n",
    "    df[\"HPrM\"], df[\"APrM\"] = zip(\n",
    "        *df.progress_apply(helperFunctions.numDaysSinceLastMatch, axis=1)\n",
    "    )\n",
    "\n",
    "    # impute\n",
    "    df[\"B365H\"], df[\"B365D\"], df[\"B365A\"] = zip(\n",
    "        *df.progress_apply(helperFunctions.extractAverageBettingOddsIfMissing, axis=1)\n",
    "    )\n",
    "    (\n",
    "        df[\"SHF\"],\n",
    "        df[\"SHST\"],\n",
    "        df[\"SHY\"],\n",
    "        df[\"SHR\"],\n",
    "        df[\"SHTHG\"],\n",
    "        df[\"SAF\"],\n",
    "        df[\"SAST\"],\n",
    "        df[\"SAY\"],\n",
    "        df[\"SAR\"],\n",
    "        df[\"SHTAG\"],\n",
    "    ) = zip(*df.progress_apply(helperFunctions.numSTYRHTGInLastFiveMatches, axis=1))\n",
    "    df[\"B365pred\"] = df.progress_apply(helperFunctions.getBet365prediction, axis=1)\n",
    "    df[\"B365HDstDrw\"] = df[\"B365D\"] / df[\"B365H\"]\n",
    "    df[\"B365ADstDrw\"] = df[\"B365D\"] / df[\"B365A\"]\n",
    "    df[\"RstDayDiff\"] = df[\"HPrM\"] - df[\"APrM\"]\n",
    "\n",
    "    columns_to_drop = [\n",
    "        \"B365H\",\n",
    "        \"B365D\",\n",
    "        \"B365A\",\n",
    "        \"HPrM\",\n",
    "        \"APrM\",\n",
    "        \"AC\",\n",
    "        \"AF\",\n",
    "        \"AR\",\n",
    "        \"AS\",\n",
    "        \"AST\",\n",
    "        \"AY\",\n",
    "        \"HC\",\n",
    "        \"HF\",\n",
    "        \"HR\",\n",
    "        \"HS\",\n",
    "        \"HST\",\n",
    "        \"HY\",\n",
    "        \"HTAG\",\n",
    "        \"HTHG\",\n",
    "    ]\n",
    "    columns_to_drop = [col for col in columns_to_drop if col in df.columns]\n",
    "    df = df.drop(columns=columns_to_drop)\n",
    "    return df\n",
    "\n",
    "train_df = extract_and_summarize(train_df)\n",
    "test_df = extract_and_summarize(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop non-numeric columns\n",
    "numeric_columns = train_df.select_dtypes(include=['float64', 'int64']).columns\n",
    "train_df_numeric = train_df[numeric_columns]\n",
    "\n",
    "# Plot the heatmap\n",
    "plt.figure(figsize=(12, 10), dpi=125)\n",
    "sns.heatmap(train_df_numeric.corr(), annot=True, fmt=\".1f\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>league</th>\n",
       "      <th>HPr5MW</th>\n",
       "      <th>HPr5MD</th>\n",
       "      <th>HPr5ML</th>\n",
       "      <th>APr5MW</th>\n",
       "      <th>APr5MD</th>\n",
       "      <th>APr5ML</th>\n",
       "      <th>SHS</th>\n",
       "      <th>SHST</th>\n",
       "      <th>SHY</th>\n",
       "      <th>SHR</th>\n",
       "      <th>SHTHG</th>\n",
       "      <th>SAS</th>\n",
       "      <th>SAST</th>\n",
       "      <th>SAY</th>\n",
       "      <th>SAR</th>\n",
       "      <th>SHTAG</th>\n",
       "      <th>B365pred</th>\n",
       "      <th>B365HDstDrw</th>\n",
       "      <th>B365ADstDrw</th>\n",
       "      <th>RstDayDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>15/01/2023</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>E0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1.161290</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Almere City</td>\n",
       "      <td>04/04/2024</td>\n",
       "      <td>Waalwijk</td>\n",
       "      <td>N1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>25/02/2024</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>E0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>H</td>\n",
       "      <td>3.298611</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Annecy</td>\n",
       "      <td>25/02/2023</td>\n",
       "      <td>Sochaux</td>\n",
       "      <td>F2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>H</td>\n",
       "      <td>2.035928</td>\n",
       "      <td>0.591304</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Panathinaikos</td>\n",
       "      <td>28/01/2024</td>\n",
       "      <td>PAOK</td>\n",
       "      <td>G1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>1.015385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7126</th>\n",
       "      <td>Northampton</td>\n",
       "      <td>19/11/2022</td>\n",
       "      <td>Bradford</td>\n",
       "      <td>E3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7127</th>\n",
       "      <td>Almeria</td>\n",
       "      <td>01/03/2024</td>\n",
       "      <td>Celta</td>\n",
       "      <td>SP1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.945946</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7128</th>\n",
       "      <td>Brest</td>\n",
       "      <td>17/12/2023</td>\n",
       "      <td>Nantes</td>\n",
       "      <td>F1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.216730</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7129</th>\n",
       "      <td>FC Emmen</td>\n",
       "      <td>22/04/2023</td>\n",
       "      <td>Heerenveen</td>\n",
       "      <td>N1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>2.117647</td>\n",
       "      <td>0.654545</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7130</th>\n",
       "      <td>Paris SG</td>\n",
       "      <td>15/01/2023</td>\n",
       "      <td>Rennes</td>\n",
       "      <td>F1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>3.025478</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7131 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AwayTeam        Date    HomeTeam league  HPr5MW  HPr5MD  HPr5ML  \\\n",
       "0              Arsenal  15/01/2023   Tottenham     E0       2       1       2   \n",
       "1          Almere City  04/04/2024    Waalwijk     N1       1       2       2   \n",
       "2     Sheffield United  25/02/2024      Wolves     E0       2       1       2   \n",
       "3               Annecy  25/02/2023     Sochaux     F2       3       2       0   \n",
       "4        Panathinaikos  28/01/2024        PAOK     G1       4       0       1   \n",
       "...                ...         ...         ...    ...     ...     ...     ...   \n",
       "7126       Northampton  19/11/2022    Bradford     E3       2       3       0   \n",
       "7127           Almeria  01/03/2024       Celta    SP1       1       1       3   \n",
       "7128             Brest  17/12/2023      Nantes     F1       1       1       3   \n",
       "7129          FC Emmen  22/04/2023  Heerenveen     N1       2       1       2   \n",
       "7130          Paris SG  15/01/2023      Rennes     F1       2       1       2   \n",
       "\n",
       "      APr5MW  APr5MD  APr5ML   SHS  SHST   SHY  SHR  SHTHG   SAS  SAST   SAY  \\\n",
       "0          4       1       0  64.0  29.0   8.0  0.0    1.0  75.0  22.0  11.0   \n",
       "1          0       4       1  70.0  20.0   6.0  0.0    1.0  57.0  24.0  12.0   \n",
       "2          1       1       3  67.0  29.0  11.0  0.0    3.0  53.0  20.0  11.0   \n",
       "3          2       2       1  63.0  24.0  10.0  1.0    4.0  53.0  21.0   7.0   \n",
       "4          4       1       0  71.0  38.0   6.0  0.0    8.0  64.0  27.0   8.0   \n",
       "...      ...     ...     ...   ...   ...   ...  ...    ...   ...   ...   ...   \n",
       "7126       2       3       0  69.0  23.0   7.0  1.0    3.0  54.0  18.0  11.0   \n",
       "7127       0       3       2  45.0  21.0   8.0  0.0    3.0  66.0  23.0  14.0   \n",
       "7128       3       1       1  75.0  18.0   8.0  0.0    2.0  61.0  19.0  10.0   \n",
       "7129       2       1       2  55.0  25.0   8.0  0.0    6.0  56.0  23.0   5.0   \n",
       "7130       4       0       1  59.0  22.0   6.0  3.0    3.0  58.0  32.0   9.0   \n",
       "\n",
       "      SAR  SHTAG B365pred  B365HDstDrw  B365ADstDrw  RstDayDiff  \n",
       "0     0.0    2.0        A     1.161290     1.636364          -1  \n",
       "1     1.0    4.0        H     1.636364     1.125000           1  \n",
       "2     2.0    5.0        H     3.298611     0.678571           1  \n",
       "3     0.0    5.0        H     2.035928     0.591304           0  \n",
       "4     0.0    2.0        H     1.466667     1.015385           0  \n",
       "...   ...    ...      ...          ...          ...         ...  \n",
       "7126  2.0    4.0        H     1.500000     0.970588           0  \n",
       "7127  1.0    2.0        H     1.945946     0.857143          -1  \n",
       "7128  1.0    4.0        H     1.216730     1.142857           1  \n",
       "7129  0.0    0.0        H     2.117647     0.654545           1  \n",
       "7130  1.0    5.0        A     0.950000     3.025478           0  \n",
       "\n",
       "[7131 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df # all columns are displayed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop non-numeric columns\n",
    "numeric_columns = train_df.select_dtypes(include=['float64', 'int64']).columns\n",
    "train_df_numeric = train_df[numeric_columns]\n",
    "\n",
    "# Plot the correlation heatmap\n",
    "plt.figure(figsize=(12, 10), dpi=125)\n",
    "sns.heatmap(train_df_numeric.corr(), annot=True, fmt=\".1f\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null values in train_df = 91\n",
      "Number of null values in test_df = 15\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of null values in train_df = {train_df.isnull().sum().sum()}\")\n",
    "print(f\"Number of null values in test_df = {test_df.isnull().sum().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'SHF'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'SHF'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 18\u001b[0m\n\u001b[1;32m     12\u001b[0m     df \u001b[38;5;241m=\u001b[39m df[[\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDF\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDST\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDY\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDR\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDPr5MW\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDPr5MD\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDPr5ML\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHTDG\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m     14\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRstDayDiff\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB365DstDrw\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFTR\u001b[39m\u001b[38;5;124m'\u001b[39m  ]]\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m df\n\u001b[0;32m---> 18\u001b[0m train_df \u001b[38;5;241m=\u001b[39m \u001b[43mcompute_difference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m test_df \u001b[38;5;241m=\u001b[39m compute_difference(test_df)\n",
      "Cell \u001b[0;32mIn[10], line 2\u001b[0m, in \u001b[0;36mcompute_difference\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute_difference\u001b[39m(df):\n\u001b[0;32m----> 2\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDF\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSHF\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m-\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSAF\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      3\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDST\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSHST\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m-\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSAST\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      4\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDY\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSHY\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m-\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSAY\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3760\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3763\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3657\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3658\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3659\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3660\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'SHF'"
     ]
    }
   ],
   "source": [
    "def compute_difference(df):\n",
    "    df['DF'] = -(df['SHF'] - df['SAF'])\n",
    "    df['DST'] = df['SHST'] - df['SAST']\n",
    "    df['DY'] = -(df['SHY'] - df['SAY'])\n",
    "    df['DR'] = -(df['SHR'] - df['SAR'])\n",
    "    df['DPr5MW'] = df['HPr5MW'] - df['APr5MW']\n",
    "    df['DPr5MD'] = df['HPr5MD'] - df['APr5MD']\n",
    "    df['DPr5ML'] = df['HPr5ML'] - df['APr5ML']\n",
    "    df['HTDG'] = df['SHTHG'] - df['SHTAG']\n",
    "    df['B365DstDrw'] = df.progress_apply(helperFunctions.maxDstDrw, axis=1)\n",
    "    # we can drop the columns we have extracted these new columns from and rearrange to appear as they did before\n",
    "    df = df[[\n",
    "    'DF', 'DST', 'DY', 'DR', 'DPr5MW', 'DPr5MD', 'DPr5ML', 'HTDG', \n",
    "    'RstDayDiff', 'B365DstDrw', 'FTR'  ]]\n",
    "        \n",
    "    return df\n",
    "\n",
    "train_df = compute_difference(train_df)\n",
    "test_df = compute_difference(test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the train_df and test_df as CSV\n",
    "if not os.path.exists('feature_engineered_data'):\n",
    "    os.mkdir('feature_engineered_data')\n",
    "\n",
    "train_df.to_csv(os.path.join(\"feature_engineered_data\", 'train.csv'), index=False)\n",
    "test_df.to_csv(os.path.join(\"feature_engineered_data\", 'test-3.csv'), index=False)"
   ]
  }
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